提高缺乏技术的有效性:在土壤气体监测中用于环境变量分析的机器学习应用
David Lorenzo1, Fernando Barrio-Parra2, Alessandra Cecconi3
1Department of Chemical Engineering and Materials, Facultad de Ciencias Químicas, Universidad Complutense de Madrid, Avenida Complutense S/N, 28040, Madrid, Spain. dlorenzo@quim.ucm.es.
Environmental science and pollution research international
|October 15, 2025
概括
机器学习模型改进了用于评估土壤污染的缺乏技术 (RDT). 通过分析环境因素,这些模型提高了识别受污染地点的准确性,有助于修复工作.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 数据科学数据科学数据科学
背景情况:
- 土壤污染是一个重要的环境问题,需要有效的地点表征和修复策略.
- 缺乏技术 (RDT) 使用-222 (222Rn) 作为一种可追踪剂,用于非侵入性检测有机污染.
- 诸如土壤水分,温度和大气压等环境因素会给RDT测量带来不确定性.
研究的目的:
- 评估机器学习 (ML) 模型在预测土壤气体222Rn活动中的有效性.
- 用ML. 评估环境变量对222Rn活动的影响.
- 确定ML是否可以提高污染评估中缺乏技术的可靠性.
主要方法:
- 从一个基于花岩的地点收集了一年的持续环境测量数据集.
- 使用的机器学习模型:线性回归 (LR),随机森林 (RF),人工神经网络 (ANN) 和梯度增强机器 (GBM).
- 输入变量包括土壤湿度,环境和土壤温度,以及大气条件来预测222Rn活动.
主要成果:
- 人工神经网络 (ANN) 和随机森林 (RF) 模型在预测222Rn变异性方面表现出卓越的表现.
- 土壤湿度和环境温度被确定为222Rn活动的最重要的预测因素.
- 机器学习模型有效考虑了环境变化,提高了研发与开发技术的可靠性.
结论:
- 通过减轻环境不确定性,机器学习显著提高了缺乏技术的可靠性.
- 机器学习模型为更准确地识别污染热点和有效的整治监测提供了一个有前途的工具.
- 需要进一步的研究来探索ML在各种地质环境和受污染地点的应用.
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